Results for “geo”

Expert knowledge for Azure Cache for Redis development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when configuring geo-replicated caches, Private Link access, persistence, CLI/PowerShell automation, or ARM/Bicep deployments, and other Azure Cache for Redis related development tasks. Not for Azure Managed Redis (use azure-managed-redis), Azure HPC Cache (use azure-hpc-cache), Azure Blob Storage (use azure-blob-storage), Azure Table Storage (use azure-table-storage).

Use for writing, reviewing, debugging, testing, or optimizing Python code that inspects, edits, extracts, validates, preserves, or generates Microsoft PowerPoint Open XML presentations, primarily .pptx, using python-pptx, PresentationML/OOXML, Pillow, or supporting Python libraries. Trigger on slides, masters, layouts, placeholders, shapes, text, pictures, tables, charts, notes, themes, hyperlinks, embedded objects, macros, preservation, geometry, rendering verification, and presentation package inspection. Do not use for .ppt binary files, PowerPoint UI automation, VBA execution, slideshow execution, or presentation advice with no Python or file boundary.

Export pcbComponent designs to Gerber files with RF connectors and fab service formatting for PCB manufacturing. TRIGGER: user asks to export a PCB design to Gerber, generate manufacturing files, or write out a pcbComponent for fabrication. Invoke BEFORE writing export code — gerberWrite signature (pcbComponent, connectors, filename) is non-obvious. SKIP: importing/reading PCB files (use matlab-read-pcb-layout), building PCB geometry (use matlab-assemble-pcb-layout), EM analysis (use matlab-analyze-em), material/stackup setup (use matlab-manage-pcb-material).

Runs high-performance genomic interval analysis with gtars (databio), a Rust toolkit with Python bindings — the performance-critical backend for the geniml ML library. Use when computing overlaps/jaccard/coverage between BED region sets, indexing intervals with IGD, generating uniwig accumulation/coverage tracks, tokenizing genomic regions for ML, splitting single-cell fragments into pseudobulks, or computing GA4GH refget sequence digests. NOT for training region embeddings (use alterlab-geniml) or non-genomic spatial joins (use alterlab-geopandas). Part of the AlterLab Academic Skills suite.

Expert knowledge for Azure Event Hubs development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Kafka clients/Streams, .NET SDK, Flink/Spark, geo-DR/replication, or Auto Inflate scaling in Event Hubs, and other Azure Event Hubs related development tasks. Not for Azure Service Bus (use azure-service-bus), Azure Event Grid (use azure-event-grid), Azure Notification Hubs (use azure-notification-hubs), Azure Stream Analytics (use azure-stream-analytics).

A short, design-led motion graphic where motion is the message — kinetic typography, stat count-up, chart/data-viz hit, logo sting / brand lockup, lower-third / callout / social overlay, animated map (highlight regions, connect places, zoom to a location), animated tweet / news-article / headline, webpage / UI animation (scroll, cursor, callouts), or fusing a real image's geometry into a chart. Usually under 10s (up to ~30s), no narration or live-action subject; renders to MP4 or transparent overlay. Longer / narrated / multi-scene → /general-video. Unclear → /hyperframes.

Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing topologies — applicable to social, biological, transportation, citation, and any pairwise-relationship networks. This is classical graph analytics, not deep learning — for training graph neural networks (GCN/message passing, node/edge/graph classification on Cora-style data) use alterlab-torch-geometric instead. Part of the AlterLab Academic Skills suite.

Use esta competência ao trabalhar com matemática simbólica em Python. Esta competência deve ser usada para tarefas de computação simbólica incluindo resolução de equações algebraicamente, realização de operações de cálculo (derivadas, integrais, limites), manipulação de expressões algébricas, trabalho com matrizes simbolicamente, cálculos de física, problemas de teoria dos números, computações de geometria e geração de código executável a partir de expressões matemáticas. Aplique esta competência quando o usuário precisa de resultados simbólicos exatos em vez de aproximações numéricas, ou ao trabalhar com fórmulas matemáticas que contêm variáveis e parâmetros.

Wilkinson, branchline, ratrace, directional couplers, corporate dividers, Rotman lenses for power splitting and beam-forming. TRIGGER: user asks to design, create, or analyze any coupler, splitter, power divider, combiner, or Rotman lens. Invoke BEFORE writing code — class names and design() availability vary per coupler type. SKIP: EM simulation/S-parameter extraction of an existing component (use matlab-analyze-em), building custom non-catalog geometry (use matlab-assemble-pcb-layout), material/stackup setup only (use matlab-manage-pcb-material), cascading multiple components (use matlab-integrate-pcb-circuit).

GDPR and CCPA/CPRA privacy compliance audit for codebases. Inventories PII fields (email, phone, SSN, IP, device ID, geolocation, biometrics, behavioral data), maps data collection points (forms, APIs, cookies, analytics, error tracking), audits consent mechanisms (cookie banners, opt-in, pre-checked boxes, consent withdrawal), verifies data subject rights implementation (right to access, erasure, rectification, portability, opt-out, Do Not Sell), traces third-party data sharing (Google Analytics, Facebook Pixel, Stripe, SendGrid, Sentry), and checks data retention policies and automated purging. Use when auditing privacy compliance, building data export or deletion endpoints, reviewing cookie consent, or assessing DSAR readiness.

Upgrade of chinese-ppt for Chinese Beamer decks (xelatex + ctex + fandol, CUFE template). Same 7-section outline and X→M→Y framework, but adds (a) mandatory 1.3 line-spacing + 3pt paragraph spacing, (b) overflow-prevention line budget + balanced split-frame rules, (c) prose rewrites that strip AI-ish "关键比较/理论映射" labels and redundant 章节括号注释, (d) stricter CJK-space + middle-dot rules, (e) retrofit workflow for existing chinese-ppt decks, (f) mandatory local linespread reset inside TikZ / table / fixed-geometry frames. Use for "做中文学术PPT 2 版"、"中文 Beamer 改进版"、"答辩 PPT chinese-ppt2"、"防越界 PPT"等请求。用户在 Overleaf 编译，只产出 .tex + figures/，不要附加 compile 脚本。

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' or 'optimize for Claude/Gemini.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema-markup.

Import Gerber, ODB++, Allegro .brd, .mcm files for PCB boards and IC packages. Inspect nets, layers, shapes, and stackups. TRIGGER: user asks to import, read, or open a PCB layout file. Gerber files use gerberRead or PCBReader; ODB++, Allegro .brd, .mcm, and native formats use pcbFileRead. Also when inspecting nets, layers, components, or stackups from an imported board. Invoke BEFORE writing import code — the query API (cadnet, cadnetList, componentList) is specialized. SKIP: EM analysis or S-parameter extraction (use matlab-analyze-em), PDN/IR-drop analysis (use matlab-analyze-pcb-pdn), building custom geometry (use matlab-assemble-pcb-layout), material/stackup definition only (use matlab-manage-pcb-material).

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

Build, audit, debug, and migrate websites for durable organic discovery and reliable use by search engines, LLMs, and browser agents. Use for SEO audits or implementation; new sites, routes, templates, CMSs, catalogs, or content programs; crawling, rendering, indexing, robots, canonicals, redirects, sitemaps, hreflang, structured data, metadata, links, faceted navigation, JavaScript SEO, Core Web Vitals, or migrations; keyword, intent, architecture, content, local, media, or authority work; traffic, ranking, CTR, or indexation losses; AEO, GEO, AI Overviews, AI Mode, ChatGPT search, crawler controls, agent-readable pages, Markdown representations, `Accept: text/markdown` content negotiation, `llms.txt`, WebMCP, or agent interaction design. Trigger whenever code or architecture can materially affect public discoverability or machine usability, even if the user does not say SEO. Do not use for paid-search management alone.

Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-to-operate / competitive landscape / acquisition diligence / litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.

Generate driving scenes, scenarios, road surfaces, and 3D content from scenariobuilder.* sensor data (GPS, camera, lidar, actor tracks) using Scenario Builder for Automated Driving Toolbox. BUILD, EXPORT, or AUGMENT a virtual scenario/scene/map: ego or actor trajectories, trajectory smoothing, OpenCRG road-surface extraction, 3D asset generation, static-object placement, point-cloud georeferencing + elevation, lane-based ego localization, sensor-fusion tracking, scenario-event extraction (cut-ins, hard brakes, near-misses, ADAS disengagements), or export to RoadRunner, drivingScenario, OpenDRIVE, OpenCRG, OpenSCENARIO, or Unreal Engine. Also: log-to-scenario, scenario harvesting, accident/near-miss reconstruction, SOTIF (ISO 21448) and ISO 26262 scenario coverage, USGS-aerial-lidar augmentation, traffic-sign placement, vision-based vehicle classification for actor assets. NOT for raw-data import or multi-sensor sync/crop/offset/timestamp normalization — route those to matlab-import-driving-data.

When the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions "attribution," "attribution model," "first-touch vs last-touch," "multi-touch," "which channel drives revenue," "what's my real CAC," "my dashboards disagree," "Google/Meta says X but GA says Y," "media mix model," "MMM," "incrementality," "geo lift," "holdout test," "how did you hear about us," "self-reported attribution," "dark social," or wants to instrument attribution themselves — "stitch my bookings to their source," "SavvyCal/Calendly attribution," "close the identify gap," "track conversions on a third-party domain," "first-party / self-hosted attribution." For event tracking setup and UTMs, see analytics. For ad-platform pixels/CAPI, see ads. For pipeline and CRM revenue reporting, see revops. For the AI-search attribution blind spot, see ai-seo.